Facial_Recognition.ipynb

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# importing the cv2 library
import cv2

# loading the haar case algorithm file into alg variable
alg = "haarcascade_frontalface_default.xml"
# passing the algorithm to OpenCV
haar_cascade = cv2.CascadeClassifier(alg)
# loading the image path into file_name variable - replace <INSERT YOUR IMAGE NAME HERE> with the path to your image
file_name = "/content/Friends.jpg"
# reading the image
img = cv2.imread(file_name, 0)
# creating a black and white version of the image
gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# detecting the faces
faces = haar_cascade.detectMultiScale(
    gray_img, scaleFactor=1.05, minNeighbors=2, minSize=(100, 100)
)
#gray image = actual image
#scale factor = compession amount
#minNeighbour = Neighbouring face after finding one
#minSize = minimum size of pixel

i = 0
# for each face detected
for x, y, w, h in faces:
    # crop the image to select only the face
    cropped_image = img[y : y + h, x : x + w]
    # loading the target image path into target_file_name variable  - replace <INSERT YOUR TARGET IMAGE NAME HERE> with the path to your target image
    target_file_name = 'stored-faces/' + str(i) + '.jpg'
    cv2.imwrite(
        target_file_name,
        cropped_image,
    )
    i = i + 1;

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# importing the required libraries
import numpy as np
from imgbeddings import imgbeddings
from PIL import Image
import psycopg2
import os

# connecting to the database - replace the SERVICE URI with the service URI
conn = psycopg2.connect("postgres://avnadmin:AVNS_nMbOMAfs_CDtCPoAIqt@pg-1c045a50-friends07.a.aivencloud.com:12415/defaultdb?sslmode=require")

for filename in os.listdir("stored-faces"):
    # opening the image
    img = Image.open("stored-faces/" + filename)
    # loading the `imgbeddings`
    ibed = imgbeddings()
    # calculating the embeddings
    embedding = ibed.to_embeddings(img)
    cur = conn.cursor()
    cur.execute("INSERT INTO pictures values (%s,%s)", (filename, embedding[0].tolist()))
    print(filename)
conn.commit()

/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:655: FutureWarning: 'cached_download' is the legacy way to download files from the HF hub, please consider upgrading to 'hf_hub_download'
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/transformers/models/clip/processing_clip.py:144: FutureWarning: `feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.
  warnings.warn(

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from PIL import Image
# loading the face image path into file_name variable
file_name = "/content/joey.jpg"  # replace <INSERT YOUR FACE FILE NAME> with the path to your image
# opening the image
img = Image.open(file_name)
# loading the `imgbeddings`
ibed = imgbeddings()
# calculating the embeddings
embedding = ibed.to_embeddings(img)

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